Structuring AI adoption
How do you organise a measurable AI adoption pilot before deciding whether to extend it to other business functions?
3500 € per session, excl. VAT
In short
Over two days, totalling 14 hours excluding breaks, you will build a generative AI adoption plan that brings together business priorities, governance, support and measurement. You will prepare a limited pilot, simulate its launch decisions and define the conditions for extending it across the enterprise, without confusing tool usage with the quality of use.
Why this training?
Increasing access to AI is not enough to transform working practices. Without balancing use cases, responsibilities and support capacity, local initiatives remain difficult to compare and their risks hard to see. A pilot with clear governance makes it possible to test business usefulness, output quality and adoption together. Generative AI helps prepare the materials; decisions on scope, controls and expansion remain human. Running case study: Avelune Équipements, a fictitious multi-site company, observes scattered AI use across procurement, support and human resources. Its management wants to select two pilot use cases, set rules for data use and prepare a conditional expansion despite differing maturity levels and constraints. Transfer to the workplace: Have the pilot dossier reviewed by a business sponsor, the IT department and the control functions; confirm resources, establish the baseline measurement and then decide on launch before any expansion.
Objectives of this training
- Map maturity levels, stakeholders and barriers by business function
- Prioritise use cases according to expected value, feasibility and risks
- Design governance with responsibilities and decision-making arrangements
- Build differentiated support for each target group
- Define and then test launch and expansion criteria
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Transformation directors and AI programme managers
- Business sponsors, HR managers and change management leads
- IT, data and compliance representatives involved in the rollout
Prior skills
- Have practical experience using an AI assistant and know its limitations
- Be able to describe a business process and its controls
- Understand the responsibilities within a cross-functional project
What you will work on
Diagnose adoption gaps
Objective: Distinguish between access, use and proficiency. Maturity, informal practices, multi-site constraints.
Make portfolio decisions
Objective: Select two pilot use cases. Expected value, feasibility, risks and dependencies.
Organise responsibilities
Objective: Make accountability for decisions clear. Sponsor, business functions, IT department, HR, compliance; escalations.
Set a framework for use
Objective: Define the pilot’s controls. Confidentiality, human validation, incidents and traceability.
Define the boundaries of the experiment
Objective: Make the pilot feasible. Target group, scope, resources and exclusions.
Mobilise target groups
Objective: Adapt support to resistance. Managers, local champions, training and social dialogue according to context.
Prototype day-to-day support
Objective: Equip the first users. Instructions, assistance and reporting of difficulties.
Measure without conflating effects
Objective: Separate adoption from the quality of business work. Baseline, active use, errors, rework and satisfaction.
Simulate the pilot review
Objective: Test the decision rules. Conflicting results, incidents and interpretation bias.
Prepare for conditional expansion
Objective: Safeguard the next steps. Rollout waves by business function, support capacity and approvals.
What you take away
- Adoption map and prioritised portfolio
- Pilot charter with a responsibility matrix
- Support kit for each target group
- Measurement table and expansion decision dossier
Method and assessment
- Short teaching inputs followed by immediate practice on the running case study.
- Individual work followed by work in pairs, with peer review of the outputs.
- Facilitated group review: what works, what needs correcting and what remains a human decision.
- Preparation: Identify two potential use cases and their pain points, without confidential data. Plan to use only tools authorised by the organisation; work on fictitious or anonymised cases.
Observable criteria: Each selected use case has an owner and a traceable rationale; Permitted data, human checks and escalation procedures are explicitly set out; Each target group receives support tailored to its barriers; Each indicator specifies a baseline, calculation, source and frequency; The simulated decision respects the thresholds and documents any reservations.
Frequently asked questions
Is this actually rolled out across the entire company?
No: the two days produce a pilot proposal tested through simulation.
Is a single AI tool required?
No, the choices depend on the use cases and internal permissions.
Can internal documents be used?
Only if they are anonymised and authorised; the fictitious case is sufficient for the exercises.
How do you assess success?
By assessing uptake, business quality and incidents together, using thresholds defined before the pilot.
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